Based on 10 years of research, she says collaboration skills are the rarest and often overlooked. Here’s how to get better at it:
If you're in charge of presenting a sales report every week, but do it solely on your own, that could suggest you think your opinion is the most valuable.
Don't forget to mention the names of those who contributed, as well as their expertise. This will give your report more credibility.Give people a way to learn without having to be part of every team. My research found that a desire to learn is a frequent driver of voluntary commitment. When shared publicly, they create a sense of peer pressure, because they allow the outcomes of leaders to be compared to those generated by their peers.
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Harvard women's hockey hazing allegations timelineThe Harvard women’s ice hockey program has been under scrutiny following reports of abusive behavior by coach Katey Stone and hazing within the program. Here is a timeline of the developments ⤵️
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Josh Berry was "mixing it up" on way to career-best finishAfter a difficult debut as Chase Elliott\u2019s substitute last weekend at Las Vegas, Josh Berry showed noticeable improvement Sunday at Phoenix.
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scEvoNet: a gradient boosting-based method for prediction of cell state evolution - BMC BioinformaticsBackground Exploring the function or the developmental history of cells in various organisms provides insights into a given cell type's core molecular characteristics and putative evolutionary mechanisms. Numerous computational methods now exist for analyzing single-cell data and identifying cell states. These methods mostly rely on the expression of genes considered as markers for a given cell state. Yet, there is a lack of scRNA-seq computational tools to study the evolution of cell states, particularly how cell states change their molecular profiles. This can include novel gene activation or the novel deployment of programs already existing in other cell types, known as co-option. Results Here we present scEvoNet, a Python tool for predicting cell type evolution in cross-species or cancer-related scRNA-seq datasets. ScEvoNet builds the confusion matrix of cell states and a bipartite network connecting genes and cell states. It allows a user to obtain a set of genes shared by the characteristic signature of two cell states even between distantly-related datasets. These genes can be used as indicators of either evolutionary divergence or co-option occurring during organism or tumor evolution. Our results on cancer and developmental datasets indicate that scEvoNet is a helpful tool for the initial screening of such genes as well as for measuring cell state similarities. Conclusion The scEvoNet package is implemented in Python and is freely available from https://github.com/monsoro/scEvoNet . Utilizing this framework and exploring the continuum of transcriptome states between developmental stages and species will help explain cell state dynamics.
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Prince Albert at 65 - his Olympic career, marriage to Princess Charlene and moreEverything you need to know about Prince Albert II of Monaco as he turns 65 on 14 March
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